Statistical inference on parametric part for partially linear single-index model

被引：14

作者：

Zhang RiQuan ^{[1
,2
]}

Huang ZhenSheng ^{[1
]}

机构：

[1] E China Normal Univ, Sch Finance & Stat, Shanghai 200241, Peoples R China

[2] Shanxi Datong Univ, Dept Math, Datong 037009, Peoples R China

来源：

SCIENCE IN CHINA SERIES A-MATHEMATICS | 2009年 / 52卷 / 10期

基金：

中国国家自然科学基金;

关键词：

asymptotic normality; generalized likelihood ratio; local linear method; partially linear single-index model; profile least-squares technique; wilks phenomenon; VARYING-COEFFICIENT MODELS; LIKELIHOOD RATIO TESTS; REGRESSION;

D O I：

10.1007/s11425-009-0079-6

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Statistical inference on parametric part for the partially linear single-index model (PLSIM) is considered in this paper. A profile least-squares technique for estimating the parametric part is proposed and the asymptotic normality of the profile least-squares estimator is given. Based on the estimator, a generalized likelihood ratio (GLR) test is proposed to test whether parameters on linear part for the model is under a contain linear restricted condition. Under the null model, the proposed GLR statistic follows asymptotically the chi(2)-distribution with the scale constant and degree of freedom independent of the nuisance parameters, known is Wilks phenomenon. Both simulated and real data examples are used to illustrate our proposed methods.

引用

页码：2227 / 2242

页数：16